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Download fileStructurally optimized neural fuzzy modelling for model predictive control
journal contribution
posted on 2021-12-13, 16:13 authored by Xiaoyan Hu, Yu GongYu Gong, Dezong Zhao, Wen GuThis paper investigates the local linear model tree (LOLIMOT), a typical neural fuzzy model, in the multiple-input-multiple-output model predictive control (MPC). In the conventional LOLIMOT, the structural parameters including centres and variances of its Gaussian kernels are set based on equally dividing the input data space. In this paper, after the structural parameters are initially obtained from the input space partition, they are optimized by the gradient descent search, from which the space partitions are further adjusted. This makes it better for the model structure to fit the input data statistics, leading to improved modelling performance with small model size. The MPC based on the proposed structurally optimized LOLIMOT is then implemented and verified with both numerical and diesel engine plants. Validation results show that the proposed MPC has significantly better controlling performance than the MPC based on the conventional LOLIMOT, making it an attractive solution in practice.
Funding
Towards Energy Efficient Autonomous Vehicles via Cloud-Aided Learning
Engineering and Physical Sciences Research Council
Find out more...History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
- Mechanical, Electrical and Manufacturing Engineering
Department
- Aeronautical and Automotive Engineering
Published in
IEEE Transactions on Industrial InformaticsVolume
19Issue
6Pages
7498 - 7507Publisher
Institute of Electrical and Electronics Engineers (IEEE)Version
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acceptance date
2021-11-20Publication date
2021-12-10Copyright date
2021ISSN
1551-3203eISSN
1941-0050Publisher version
Language
- en